Super-resolution Reconstruction of Transitional Boundary Layers Using a Deep Neural Network

Abstract

Numerical simulations of turbulent flow require excessive computational resources due to the multi scale characteristics of turbulence. Thus, a technique to reconstruct a high-resolution flow field from a coarse flow data can be helpful. For this purpose, various artificial neural-network-based super-resolution methods have been developed in recent years. Although previous studies reported that the super-resolution methods show remarkable performance for turbulent channel flow and homogeneous isotropic turbulence, its application for spatially developing flow with laminar, transitional, and turbulent characteristics has not been reported. In the present study, a super-resolution reconstruction method applicable for spatially developing laminar-transition-turbulent flow is developed by training the network for boundary layer flow with bypass transition. In addition, the generalization of the network for boundary layer flow with natural transition and for fully turbulent boundary layer flow is attempted. A super-resolution method based on a generative adversarial network (GAN) is employed for the study as it shows the best performance among tested network models. It is found that the developed method successfully reconstructs flow structures in transitional and early turbulent regions. In addition, statistics such as the mean velocity and the power spectral density of velocity from recovered fields show good agreement to those of DNS. Notably, the GAN model which is only trained for the bypass transition is also found to be applicable to boundary layer flow with K-type natural transition and fully developed turbulent boundary layers.